Multiobjective optimization of friction welding of UNS S32205 duplex stainless steel

Ajith, P.M.; Barik, Birendra Kumar; Sathiya, P.; Aravindan, S.

doi:10.1016/j.dt.2015.03.001

Cited by 20 publications

(7 citation statements)

References 13 publications

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“…Moreover, It has been observed that the hardness of the welding interface for all samples exhibit lower value than of the area close to it for both sides. This may be attributed to the refinement of grains occurred close to weld interface and higher amount of ferrite phase [11,13]. Beyond that, in all samples the hardness is fluctuated and then goes toward the hardness of base metals.…”

Section: Mechanical Propertiesmentioning

confidence: 82%

“…Tensile strength, toughness, micro hardness and corrosion behavior were evaluated, further in [9] the researchers found the effects of post weld heat treatment on 2205 DSSs weld. Ajith, et al [10][11][12][13] jointed two bars of 2205 DSSs and evaluated the mechanical properties and microstructure, optimized and performed several experiment to evaluate joint characterization, while Mercan, et al [14] optimized the welding parameters that affect increasing the fatigue strength of the joint between AISI 2205 and AISI 1020 bars. Caligulu, et al [15][16][17] welded AISI 4340-2205 steels and studied the impact of rotation speed and friction time, use the X-ray radiographic tests and found the weldability.…”

Section: Introductionmentioning

confidence: 99%

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Efficiency of Dissimilar Friction Welded SAF 2205 Duplex Stainless Steel and 1045 Medium Carbon Steel Joints

Ibraheem¹

2021

Int J Metall Met Phys

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This study investigated the microstructure and mechanical properties of frictional welded joints between SAF 2205 duplex stainless steel and AISI 1045 medium carbon steel. The welded joints were fabricated by varying the friction pressure, friction time, and keeping the forging pressure, forging time and rotational speed as constant to obtain a constant range of welding temperature (780-800 °C). The results revealed that as the friction pressure increases and friction time decreases, weld interface hardness value, tensile strength and ductility increases. Also the hardness profiles present that weld interfaces exhibit higher hardness values than that of the two base metals. It was found that the highest efficiency of weld joint achieved was 93%, while the lowest was 76%. In addition, all welded specimens failed in the weld region. The microstructure analyses were carried out using optical microscopy and scanning electron microscopy equipped with energy dispersive spectroscopy.

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Section: Mechanical Propertiesmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Efficiency of Dissimilar Friction Welded SAF 2205 Duplex Stainless Steel and 1045 Medium Carbon Steel Joints

Ibraheem¹

2021

Int J Metall Met Phys

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“…The primary issues associated with fusion welding are (i) the formation of deleterious phases, (ii) the generation of residual stresses and distortion, (iii) the generation of high heat input when joining dissimilar welds with a different melting point and coefficient of thermal expansion. Friction welding (FW) [1,2], friction stir processing (FSP) and friction stir welding (FSW) [3], on the other hand, are essential solid-state welding processes with significant applications in the power plant industry. Friction welding is a type of solid-state welding process for joining symmetrical shapes, such as tube to tube or rod to rod, without melting of the workpiece [4,5].…”

Section: Introductionmentioning

confidence: 99%

Realizing a Novel Friction Stir Processing-Enabled FWTPET Process for Strength Enhancement Using Firefly and PSO Methods

et al. 2020

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The friction welding of tube to tube plate using an external tool (FWTPET) is widely deployed in several industrial applications, such as aerospace, automotive, and power plants. Moreover, for achieving a better tensile strength and hardness in the weld zone, the friction stir processing (FSP) technique was incorporated into the FWTPET process for joining aluminum alloys (AA6063 tube, AA6061 tube plate). Furthermore, it has to be noted that FWTPET was applied for joining the AA6063 tube to the AA6061 tube plate, and FSP was deployed for reinforcing the weld zone with carbon nanotube (CNT) and silicon nitride (Si3N4) particles, thereby attaining the desirable mechanical properties. Subsequently, the Taguchi L25 orthogonal array was used for identifying the most influential input and output FWTPET + FSP process parameters. Furthermore, particle swarm optimization (PSO) and the firefly algorithm (FFA) were deployed for determining the optimized input and output FWTPET + FSP process parameters. The input process parameters include CNT, Si3N4, rotational tool speed, and depth. Furthermore, the tensile strength of the welded joint was considered as the output process parameter. The process parameters predicted by PSO and FFA were compared with the experimental values. It was witnessed that deviation between the predicted and experimental values was minimal. Moreover, it was found that FFA provided a superior tensile strength prediction than PSO.

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“…The percentage error between predicted and actual output parameters was negligibly small. The same approach was selected by Ajith et al [21] in their studies pertaining to multi-objective optimization of friction welding parameters for UNS S32205 steel. Four different welding parameters were selected for optimization, and PSO technique was used.…”

Section: Introductionmentioning

confidence: 99%

Attaining optimized A-TIG welding parameters for carbon steels by advanced parameter-less optimization techniques: with experimental validation

Vora

Abhishek

Srinivasan

2019

J Braz. Soc. Mech. Sci. Eng.

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Enhanced penetration potential of activated TIG (A-TIG) welding process over conventional TIG welding has made the former process preferred for welding in recent times. The quality and shape of the weld in A-TIG are not only influenced by the chemical composition of the flux, but also by the selection of welding parameters. As a variety of process parameters influence the results, a proper understanding of process performance and identification of favorable welding conditions (optimum setting of process parameters) are indeed necessary to improve quality. The present work highlights the application potential of multi-response optimization route by integrating response surface methodology with the JAYA optimization algorithm, particularly for optimizing the A-TIG welding process parameters for carbon steels. Systematic experiments were carried out considering welding current, arc gap and travel speed as input parameters, whereas the depth of penetration, depth-to-width ratio, heat input and total width of heat-affected zone were considered as output performance characteristics. An attempt has been made in the current study to recognize the precise setting of selected input process parameters for simultaneous optimization of the aforementioned performance characteristics. The result of JAYA algorithm has also been compared with the teaching-learning-based optimization technique. While fairly similar results were achieved, the implementation of JAYA algorithm was computationally efficient. Experimental validation of the single-objective as well as multi-objective optimization results indicates that the empirical models for the response parameters as well as the proposed optimization framework are accurate tools in A-TIG welding research.

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Multiobjective optimization of friction welding of UNS S32205 duplex stainless steel

Cited by 20 publications

References 13 publications

Efficiency of Dissimilar Friction Welded SAF 2205 Duplex Stainless Steel and 1045 Medium Carbon Steel Joints

Efficiency of Dissimilar Friction Welded SAF 2205 Duplex Stainless Steel and 1045 Medium Carbon Steel Joints

Realizing a Novel Friction Stir Processing-Enabled FWTPET Process for Strength Enhancement Using Firefly and PSO Methods

Attaining optimized A-TIG welding parameters for carbon steels by advanced parameter-less optimization techniques: with experimental validation

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